CN114333386A - Navigation information pushing method and device and storage medium - Google Patents
Navigation information pushing method and device and storage medium Download PDFInfo
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Abstract
The application discloses a navigation information pushing method, a navigation information pushing device and a storage medium, relates to the technical field of computers, and aims to prevent traffic jam by pushing navigation information used for prompting a vehicle to replan a driving path to the vehicle with a traffic state affected by abnormal traffic. The method comprises the following steps: determining a traffic anomaly type based on a target image acquired by at least one unmanned aerial vehicle; determining a target training model from a preset training model set according to the traffic anomaly type, and processing a target image by adopting the target training model to obtain an image processing result; and determining the target vehicle according to the travel information of the candidate vehicle and the image processing result, and pushing navigation information to the target vehicle, wherein the navigation information is used for prompting the target vehicle to replan the driving path.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to a method and a device for pushing navigation information and a storage medium.
Background
At present, in the driving process of a vehicle, a background server can push some navigation information to a vehicle-mounted device or a mobile terminal, and a driver in the vehicle can acquire road condition information based on the pushed navigation information and drive the vehicle to advance according to the road condition information. For example, the navigation information may include branch information, traffic light information, speed limit information, and the like in a road segment ahead, and the driver may plan a driving path in advance or control the vehicle to decelerate, and the like based on the information.
However, in the conventional navigation information push method, only the fixed information can be pushed, and some information of an emergency cannot be pushed to the vehicle, so that traffic jam often occurs when an emergency occurs.
Disclosure of Invention
The application provides a method and a device for pushing navigation information and a storage medium, wherein the navigation information used for prompting a vehicle to replan a driving path is pushed to the vehicle with a traffic abnormal influence on a traffic state, so that a traffic jam phenomenon can be avoided.
In order to achieve the purpose, the technical scheme is as follows:
in a first aspect, the present application provides a method for pushing navigation information, including: determining a traffic anomaly type based on a target image acquired by at least one unmanned aerial vehicle; determining a target training model from a preset training model set according to the traffic anomaly type, and processing a target image by adopting the target training model to obtain an image processing result; and determining the target vehicle according to the travel information of the candidate vehicle and the image processing result, and pushing the navigation information to the target vehicle. The navigation information is used for prompting the target vehicle to replan the driving path.
In the technical scheme that this application provided, because the degree of influence of the traffic anomaly of different grade type to the traffic state of road is different, so this application can be based on the target image of unmanned aerial vehicle collection at first determines the traffic anomaly type. Then, a target training model for processing the images of the traffic anomaly type is determined from a preset training model set based on the traffic anomaly type, and an image processing result is obtained. In addition, the degree of influence on the vehicles of different travel information due to different types of traffic abnormalities is also different. Therefore, the method and the device can determine the target vehicle needing to plan the driving path again by combining the travel information of the candidate vehicle and the image processing result of the target image by the target training model, and push the navigation information of the target vehicle, wherein the navigation information is used for prompting the target vehicle to plan the driving path again. According to the technical scheme, the influence results of different types of traffic abnormity on the traffic states of different vehicles can be determined, and the navigation information can be pushed to the target vehicle with the influenced traffic state so as to prompt the target vehicle to replan the driving path. Therefore, when the traffic is abnormal, the driver of the target vehicle can bypass the road section with abnormal traffic according to the prompt of the navigation information, so that the traffic jam can be avoided.
Optionally, in a possible design, the image processing result at least includes a traffic abnormality position, and the "determining the target vehicle according to the trip information of the candidate vehicle and the image processing result" may include:
determining whether the candidate vehicle passes through the abnormal traffic position within the target time length according to the travel information;
and if the traffic abnormal position of the candidate vehicle passing through the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design, when the traffic abnormality type is that an obstacle appears on the road, the traffic abnormality position is a position of the obstacle, the image processing result further includes a result of influence of the obstacle on vehicle passing, and the "determining the target vehicle according to the trip information of the candidate vehicle and the image processing result" may include:
determining whether the candidate vehicle passes through the position of the obstacle in the target time length according to the travel information under the condition that the influence result is that the vehicle passes through is influenced;
and if the position of the candidate vehicle passing through the obstacle in the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design, the "determining the traffic abnormality type based on the target image acquired by the at least one drone" may include:
determining the target number of unmanned aerial vehicles scheduled for the current area according to the number of signed vehicles and/or the current traffic flow in the current area;
scheduling the unmanned aerial vehicles for the current area according to the target number and the initial number of the unmanned aerial vehicles in the current area;
controlling the unmanned aerial vehicle in the current region to acquire a target image based on a preset navigation path in the current region after scheduling;
and determining the traffic abnormality type according to the target image.
Optionally, in another possible design, before the "determining the target vehicle according to the trip information of the candidate vehicle and the image processing result", the method may further include:
extracting the characteristics of the target image to obtain a vehicle identity in the target image;
and under the condition that the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database, determining the vehicle corresponding to the vehicle identity in the target image as a candidate vehicle.
Optionally, in another possible design, the method for pushing navigation information provided by the present application may further include:
deleting the stored target image according to a preset rule under the condition that no traffic abnormality is determined based on the target image;
or sending a first control instruction to the unmanned aerial vehicle for acquiring the target image; the first control instruction is used for indicating the unmanned aerial vehicle for acquiring the target image to delete the stored target image according to a preset rule.
Optionally, in another possible design, the method for pushing navigation information provided by the present application may further include:
under the condition that the congested road section is determined, determining a target unmanned aerial vehicle according to task information of at least one unmanned aerial vehicle;
sending a second control instruction to the target unmanned aerial vehicle; the second control instruction comprises the position of the congested road section, and the second control instruction is used for indicating the target unmanned aerial vehicle to fly to the position of the congested road section to acquire the target image.
In a second aspect, the present application provides a navigation information pushing device, including a determining module and a pushing module; .
The determining module is used for determining the traffic abnormity type based on the target image acquired by at least one unmanned aerial vehicle;
the determining module is further used for determining a target training model from a preset training model set according to the traffic anomaly type, and processing a target image by adopting the target training model to obtain an image processing result;
the pushing module is used for determining a target vehicle according to the travel information of the candidate vehicle and the image processing result determined by the determining module and pushing navigation information to the target vehicle; the navigation information is used for prompting the target vehicle to replan the driving path.
Optionally, in a possible design, the image processing result at least includes a traffic anomaly location, and the determining module is specifically configured to:
determining whether the candidate vehicle passes through the abnormal traffic position within the target time length according to the travel information;
and if the traffic abnormal position of the candidate vehicle passing through the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design, when the traffic abnormality type is that an obstacle appears on a road, the traffic abnormality position is a position of the obstacle, the image processing result further includes a result of an influence of the obstacle on vehicle passage, and the determining module is specifically configured to:
determining whether the candidate vehicle passes through the position of the obstacle in the target time length according to the travel information under the condition that the influence result is that the vehicle passes through is influenced;
and if the position of the candidate vehicle passing through the obstacle in the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design manner, the determining module is specifically configured to:
determining the target number of unmanned aerial vehicles scheduled for the current area according to the number of signed vehicles and/or the current traffic flow in the current area;
scheduling the unmanned aerial vehicles for the current area according to the target number and the initial number of the unmanned aerial vehicles in the current area;
controlling the unmanned aerial vehicle in the current region to acquire a target image based on a preset navigation path in the current region after scheduling;
and determining the traffic abnormality type according to the target image.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a feature extraction module;
the characteristic extraction module is used for extracting the characteristics of the target image to acquire the vehicle identity in the target image before the determination module determines the target vehicle according to the travel information of the candidate vehicle and the image processing result;
the determining module is further used for determining the vehicle corresponding to the vehicle identity in the target image as the candidate vehicle under the condition that the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a deleting module and a sending module;
the deleting module is used for deleting the stored target image according to a preset rule under the condition that no traffic abnormality is determined based on the target image;
or the deleting module is used for calling the sending module to send a first control instruction to the unmanned aerial vehicle for collecting the target image; the first control instruction is used for indicating the unmanned aerial vehicle for acquiring the target image to delete the stored target image according to a preset rule.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a sending module;
the determining module is used for determining a target unmanned aerial vehicle according to the task information of at least one unmanned aerial vehicle under the condition that the congested road section is determined;
the sending module is used for sending a second control instruction to the target unmanned aerial vehicle; the second control instruction comprises the position of the congested road section, and the second control instruction is used for indicating the target unmanned aerial vehicle to fly to the position of the congested road section to acquire the target image.
In a third aspect, the present application provides a navigation information pushing apparatus, including a memory, a processor, a bus, and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through a bus; when the pushing device of the navigation information is operated, the processor executes the computer-executable instructions stored in the memory to cause the pushing device of the navigation information to execute the pushing method of the navigation information as provided in the first aspect.
Optionally, the pushing device of the navigation information may further include a transceiver, and the transceiver is configured to perform the step of transceiving data, signaling or information under the control of the processor of the pushing device of the navigation information, for example, send a second control instruction to the target drone.
Further optionally, the navigation information pushing device may be a physical machine used for implementing the pushing of the navigation information, or may be a part of a device in the physical machine, for example, a system on chip in the physical machine. The push device for supporting navigation information implements the functions referred to in the first aspect, for example, receives, sends or processes data and/or information referred to in the push method for navigation information. The chip system includes a chip and may also include other discrete devices or circuit structures.
In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions are stored, and when the instructions are executed by a computer, the computer is caused to execute the method for pushing navigation information provided in the first aspect.
In a fifth aspect, the present application provides a computer program product comprising computer instructions which, when run on a computer, cause the computer to perform the method for pushing navigation information as provided in the first aspect.
It should be noted that all or part of the computer instructions may be stored on the computer readable storage medium. The computer-readable storage medium may be packaged with a processor of a navigation information pushing device, or may be packaged separately from the processor of the navigation information pushing device, which is not limited in this application.
For the descriptions of the second, third, fourth and fifth aspects in this application, reference may be made to the detailed description of the first aspect; in addition, for the beneficial effects described in the second aspect, the third aspect, the fourth aspect and the fifth aspect, reference may be made to beneficial effect analysis of the first aspect, and details are not repeated here.
In the present application, the names of the above-mentioned push devices of navigation information do not limit the devices or function modules themselves, and in practical implementations, these devices or function modules may appear by other names. Insofar as the functions of the respective devices or functional modules are similar to those of the present application, they fall within the scope of the claims of the present application and their equivalents.
These and other aspects of the present application will be more readily apparent from the following description.
Drawings
Fig. 1 is a schematic flowchart of a method for pushing navigation information according to an embodiment of the present disclosure;
fig. 2 is a schematic view of a road obstacle scene according to an embodiment of the present disclosure;
fig. 3 is a schematic flowchart of another navigation information pushing method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another method for pushing navigation information according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another method for pushing navigation information according to an embodiment of the present application;
fig. 6 is a schematic flowchart of another method for pushing navigation information according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a navigation information pushing apparatus according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of another navigation information pushing apparatus according to an embodiment of the present application.
Detailed Description
The following describes in detail a method, an apparatus, and a storage medium for pushing navigation information provided in an embodiment of the present application with reference to the accompanying drawings.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
The terms "first" and "second" and the like in the description and drawings of the present application are used for distinguishing different objects or for distinguishing different processes for the same object, and are not used for describing a specific order of the objects.
Furthermore, the terms "including" and "having," and any variations thereof, as referred to in the description of the present application, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
It should be noted that in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the description of the present application, the meaning of "a plurality" means two or more unless otherwise specified.
At present, in the driving process of a vehicle, a background server can push some navigation information to a vehicle-mounted device or a mobile terminal, and a driver in the vehicle can acquire road condition information based on the pushed navigation information and drive the vehicle to advance according to the road condition information. For example, the navigation information may include branch information, traffic light information, speed limit information, and the like in a road segment ahead, and the driver may plan a driving path in advance or control the vehicle to decelerate, and the like based on the information.
However, in the conventional navigation information push method, only the fixed information can be pushed, and some information of an emergency cannot be pushed to the vehicle, so that traffic jam often occurs when an emergency occurs.
In view of the problems in the prior art, the embodiment of the application provides a method for pushing navigation information, which can determine the influence results of different types of traffic abnormalities on the traffic states of different vehicles, and can push the navigation information to a target vehicle with the influenced traffic state to prompt the target vehicle to replan a driving path. Therefore, when the traffic is abnormal, the driver of the target vehicle can bypass the road section with abnormal traffic according to the prompt of the navigation information, so that the traffic jam can be avoided.
The navigation information pushing method provided by the embodiment of the application can be applied to a navigation information pushing device, and the navigation information pushing device can be a physical machine (such as a server) or a Virtual Machine (VM) deployed on the physical machine. The navigation information pushing device is used for acquiring a target image acquired by the unmanned aerial vehicle and then pushing navigation information to a target vehicle based on a processing result of the target image and the travel information of each vehicle.
The following describes in detail a method for pushing navigation information provided in an embodiment of the present application with reference to the accompanying drawings.
Referring to fig. 1, a method for pushing navigation information provided in the embodiment of the present application includes S101 to S103:
s101, determining the traffic abnormity type based on the target image acquired by at least one unmanned aerial vehicle.
In the embodiment of the application, in order to timely acquire the target image of the abnormal traffic place, the target image can be acquired based on the unmanned aerial vehicle. In addition, because different types of traffic anomalies have different degrees of influence on the traffic state of the road, the types of the traffic anomalies can be determined based on the target image acquired by the unmanned aerial vehicle. For example, the traffic abnormality type may be a road obstacle, or may be a traffic accident or the like.
For example, a model for identifying the traffic anomaly type may be trained in advance according to the sample image and the traffic anomaly type of the sample image, and then the traffic anomaly type corresponding to the target image may be determined based on invoking the model. It can be understood that, specifically, the algorithm and the training process for training the model for identifying the traffic abnormality type based on the sample image and the traffic abnormality type of the sample image may refer to related descriptions in the prior art, and the embodiment of the present application is not described herein again.
Optionally, in a possible implementation manner, the navigation information pushing device may determine the target number of the unmanned aerial vehicles scheduled for the current area according to the number of signed vehicles and/or the current traffic flow in the current area; then, according to the target number and the initial number of the unmanned aerial vehicles in the current area, the unmanned aerial vehicles are scheduled for the current area; and then, the unmanned aerial vehicle in the current region after scheduling can be controlled to acquire a target image based on a preset navigation path in the current region, and the traffic abnormity type is determined according to the target image.
The preset navigation path may be a cruise path determined for the drone in advance. Illustratively, each unmanned aerial vehicle can come and go the flight according to fixed navigation route, through adjusting flying height and collection field of vision in flight process, realizes gathering the road surface image in the appointed within range, and the collection scope of a plurality of unmanned aerial vehicles in every region sum can cover this region.
In order to realize the reasonable scheduling of unmanned aerial vehicles in each region under the condition that the total number of unmanned aerial vehicles is limited, the embodiment of the application can dynamically adjust the number of unmanned aerial vehicles in each region based on the traffic flow in each region. For example, an initial number of drones deployed within each zone may be determined based on historical traffic data for each zone, and then the initial number may be adjusted based on current traffic within each zone. For example, when the traffic flow increases in the current area at a certain time, a certain number of unmanned aerial vehicles need to be continuously scheduled for the current area on the basis of the number of the current unmanned aerial vehicles. Specifically, the corresponding relation between the traffic flow and the target number of the unmanned aerial vehicles can be determined in advance, and after the traffic flow of the current area is determined, the target number of the unmanned aerial vehicles needing to be deployed for the current area is determined according to the corresponding relation between the traffic flow and the target number of the unmanned aerial vehicles. And then, the number of the unmanned aerial vehicles needing to be rescheduled for the current area can be determined according to the initial number and the target number of the unmanned aerial vehicles in the current area. For another example, when the traffic flow in the current area decreases at a certain time, a part of the drones deployed in the current area may be tuned back to the drone docking station, or a part of the drones deployed in the current area may be tuned to an area with more traffic flow to perform the task of acquiring the target image.
In addition, the embodiment of the application can be applied to a scene of pushing the navigation information of the online appointment car, so that the navigation information pushing device can also dynamically adjust the number of unmanned aerial vehicles deployed in the current area by combining the number of signed vehicles in the current area, and further rationality of the number of unmanned aerial vehicles deployed in each area is realized.
Optionally, in a possible implementation manner, the pushing device of the navigation information may determine the target unmanned aerial vehicle according to the task information of at least one unmanned aerial vehicle when the congested road segment is determined; and then sending a second control instruction to the target unmanned aerial vehicle.
The second control instruction comprises the position of the congested road section, and the second control instruction is used for indicating the target unmanned aerial vehicle to fly to the position of the congested road section to acquire the target image. The mission information of the drone may be the operational status of the drone.
For example, the navigation information pushing device may monitor traffic flow of each road segment in each area, and when it is determined that the traffic flow of a road segment in a certain area exceeds a traffic flow threshold, the road segment may be determined as a congested road segment. Alternatively, the pushing device of the navigation information may receive feedback information from the running vehicle, and the feedback information may include the position of the congested road segment.
In this application embodiment, unmanned aerial vehicle can carry out the collection of target image through the mode of cruising based on predetermineeing the navigation path. Thus, when the traffic abnormality is determined to occur based on the target image acquired by the unmanned aerial vehicle, a period of time may have elapsed since the traffic abnormality actually occurred. Therefore, in order to improve the timeliness of determining the traffic abnormal type based on the target image, the navigation information pushing device can dispatch the unmanned aerial vehicle in an idle working state to quickly go to the position of the congested road section to acquire the target image under the condition that the congested road section is determined. Therefore, the traffic abnormal information can be acquired in time, so that the navigation information can be pushed to the target vehicle in time, and traffic jam is avoided. Or, in order to further improve the real-time performance of acquiring the traffic abnormality information, the pushing device of the navigation information may schedule the idle unmanned aerial vehicle from a position closest to the congested road segment when the congested road segment is determined.
Optionally, the navigation information pushing device may delete the stored target image according to a preset rule under the condition that no traffic abnormality is determined based on the target image; or sending a first control instruction to the unmanned aerial vehicle for acquiring the target image; the first control instruction is used for indicating the unmanned aerial vehicle for acquiring the target image to delete the stored target image according to a preset rule.
The preset rule may be that after the target image is determined to have no traffic abnormality, the corresponding target image is directly deleted, or the stored target image is cleaned once at preset intervals.
In a possible implementation manner, if the target image is stored in the push device of the navigation information, the push device of the navigation information may clean the stored target image once at preset intervals, or, after identifying the type of traffic abnormality of the target image each time, delete the corresponding target image if it is determined that there is no traffic abnormality.
In another possible implementation manner, if the target image is stored in the unmanned aerial vehicle, the unmanned aerial vehicle may clean the stored target image once every preset time interval, or after the pushing device of the navigation information identifies the traffic abnormality type of the target image, if it is determined that there is no traffic abnormality, the unmanned aerial vehicle may send a control instruction for instructing the unmanned aerial vehicle acquiring the target image to delete the stored target image.
In another possible implementation manner, after the unmanned aerial vehicle acquires the target image, the unmanned aerial vehicle can identify the traffic abnormality type of the target image by itself, and then after the identification, the unmanned aerial vehicle can delete the stored target image according to the preset rule without receiving a control instruction.
Because the storage resources of the pushing device for the unmanned aerial vehicle and the navigation information are limited, in the embodiment of the application, the acquired target image without traffic abnormality can be deleted, so that the storage resources are saved. Like this, can also improve the duration that unmanned aerial vehicle carried out the task.
S102, determining a target training model from a preset training model set according to the traffic abnormity type, and processing a target image by adopting the target training model to obtain an image processing result.
The preset training model set comprises a corresponding relation between the traffic anomaly type and a target training model; and the target training model is obtained by training according to the sample image and the processing result of the sample image.
In order to improve the accuracy of the image processing result, different training models can be adopted to process different types of traffic abnormal target images in the embodiment of the application.
S103, determining a target vehicle according to the travel information of the candidate vehicle and the image processing result, and pushing navigation information to the target vehicle.
The navigation information is used for prompting the target vehicle to replan the driving path.
Optionally, the image processing result at least includes a traffic abnormal position, and the navigation information pushing device may determine whether the candidate vehicle passes through the traffic abnormal position within the target duration according to the travel information; and if the traffic abnormal position of the candidate vehicle passing through the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in a possible implementation manner, in a case that the traffic abnormality type is a traffic accident, the traffic abnormality position may be an accident position, and the target duration may be an expected processing duration for processing the traffic accident. The pushing device of the navigation information can determine whether the candidate vehicle passes through the accident position within the expected processing duration according to the travel information; and if the passing accident position of the candidate vehicle in the estimated processing time length is determined, determining the candidate vehicle as the target vehicle.
Traffic accidents, one of the most common types of traffic anomalies, often result in traffic congestion. However, the processing time periods of different traffic accidents may be different, for example, the processing time period of a large traffic accident is longer, and the processing time period of a small traffic accident is shorter. Also, the processing time duration of traffic accidents occurring at different road segments may be different. For example, when a traffic accident occurs on a section of a road with many lanes, the time period for processing the traffic accident is relatively short, and when a traffic accident occurs on a section of a road, the time period for processing the traffic accident is relatively long. Therefore, the embodiment of the application can call the target training model to determine the expected processing time length of the traffic accident, and then can determine whether the accident can be processed or not when each vehicle runs to the accident position according to the expected processing time length and the travel information of each candidate vehicle, that is, whether the traffic accident can affect the passing of each vehicle or not can be determined according to the expected processing time length, then the vehicle which is affected by the passing can be determined as the target vehicle, and the navigation information for prompting the vehicle to replan the running path can be pushed to the target vehicle, so that the traffic jam phenomenon can be avoided.
Optionally, in another possible implementation manner, in a case that the traffic abnormality type is that an obstacle appears on the road, the traffic abnormality position is a position of the obstacle; the image processing result also comprises an influence result of the obstacles on the vehicle passing; the navigation information pushing device can determine whether the candidate vehicle passes through the position of the obstacle within the target duration according to the travel information under the condition that the influence result is that the vehicle passing is influenced; and if the position of the candidate vehicle passing through the obstacle in the target time length is determined, determining the candidate vehicle as the target vehicle.
The target time period may be a time period determined in advance by human, for example, the target time period may be 1 hour. The trip information may be the current location of the candidate vehicle and may also include a navigation route of the candidate vehicle within the target time period. If the position of the candidate vehicle passing through the obstacle in the navigation route in the target time length is determined, the position of the candidate vehicle passing through the obstacle in the target time length can be determined. Or, the maximum duration required for driving from the current position of the candidate vehicle to the position of the obstacle may be calculated according to the current position of the candidate vehicle and the position of the obstacle, and if the maximum duration is less than the target duration, the position of the candidate vehicle that may pass through the obstacle within the target duration may be determined.
When an obstacle appears on the road, the passing of the vehicle may be affected. However, different types of obstacles have different effects on the passing of the vehicle, for example, when the road is temporarily constructed, a road block may be arranged, a temporary rainwater treatment device may be arranged in rainy days, and the road block and the temporary rainwater treatment device may cause the vehicle to be unable to pass through the current lane. When objects falling from the vehicle appear on the road, the objects may or may not affect the passing of the vehicle. In addition, the size of the obstacle may affect the passing of the vehicle, and for example, when the size of the obstacle is small, the passing of the vehicle is not affected. Therefore, the embodiment of the application can acquire sample images of obstacles of different types and sizes in advance, mark the influence result of the obstacles in the sample images on the passing of the vehicle, and then obtain the target training model based on the sample images and the influence result of the sample images. Therefore, the navigation information pushing device can call the target training model to determine whether the obstacles in the target image can influence the passing of the vehicle, then can determine the vehicle with the passing influenced by the obstacles as the target vehicle, and pushes the navigation information for prompting the target vehicle to replan the driving path, so that the traffic jam phenomenon is avoided.
Illustratively, referring to fig. 2, a schematic view of a road obstacle scene is provided. If the target image acquired by the unmanned aerial vehicle only includes the obstacle a in fig. 2, the size of the obstacle a is small, so that the passing of the vehicle B is not affected, and at this time, the vehicle B is not determined as a target vehicle, that is, navigation information for prompting the vehicle B to replan the driving path is not pushed to the vehicle B. If the target image acquired by the unmanned aerial vehicle includes the obstacle B in fig. 2, the size of the obstacle B is large, so that the obstacle B affects the passing of the vehicle B, and at this time, the vehicle B can be determined as a target vehicle, and navigation information for prompting the vehicle B to replan driving path is pushed to the vehicle B.
Optionally, the candidate vehicle may be determined by the following method in an embodiment of the present application: extracting the characteristics of the target image to obtain a vehicle identity in the target image; and under the condition that the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database, determining the vehicle corresponding to the vehicle identity in the target image as a candidate vehicle.
In the scenario that the embodiment of the application is applied to navigation information pushing of the online appointment, the candidate vehicle may be a contracted vehicle in the current area.
In a possible implementation manner, the navigation information pushing device may perform feature extraction on a license plate region in a target image, acquire a vehicle identification corresponding to the target image, match the vehicle identification with an identification database containing vehicle identifications of all signed vehicles, and determine whether the vehicle is a signed vehicle.
In another possible implementation manner, a specific body identifier may be set on the body of the contracted vehicle in advance, and the pushing device of the navigation information may determine whether the vehicle is the contracted vehicle based on the specific body identifier.
Optionally, the candidate vehicle may be determined by the following method in an embodiment of the present application: and acquiring the travel information of each vehicle, and determining candidate vehicles according to the travel information of each vehicle and a preset time period.
The preset time period may be a time period determined in advance by a human.
Due to the limited resolution of the target image, the definition of the vehicle identification in the acquired target image is also limited, which may affect the accuracy of identifying the vehicle identification. Therefore, optionally, the pushing device of the navigation information may monitor the trip information of all vehicles, and then may determine vehicles that may be in the current area within a preset time period in the future according to the monitored trip information, and then may determine these vehicles as candidate vehicles. This manner of determining candidate vehicles may allow for a more accurate determination of candidate vehicles in the current area at a future time than if the candidate vehicles were determined based on the target images.
For example, in a scenario where the embodiment of the present application is applied to pushing navigation information for a network appointment, the navigation information pushing device may monitor the travel information of all the contracted vehicles, then may determine, according to the monitored travel information, contracted vehicles that may be in the current area in a future period, and then may determine these vehicles as candidate vehicles.
It can be understood that, in practical applications, the method for pushing navigation information provided by the embodiment of the present application may also be applied to other scenarios, and accordingly, the candidate vehicle may be determined in other ways. For example, the method for pushing the navigation information may be applied to a scenario in which the navigation information is pushed to a vehicle using the navigation application, and correspondingly, the candidate vehicle may be a vehicle that is using the navigation application at the current time and starts a driving mode.
For example, in the embodiment of the application, the navigation information pushing device may push the navigation information of the target vehicle by sending the navigation information to the vehicle-mounted device of the target vehicle. Or the navigation information can be sent to a mobile terminal connected with the vehicle-mounted equipment of the target vehicle, so that the navigation information of the target vehicle is pushed. The mobile terminal can be different types of terminals such as a mobile phone, a tablet computer or wearable electronic equipment.
In the technical scheme provided by the embodiment of the application, because the influence degree of traffic abnormality of different types on the traffic state of the road is different, the traffic abnormality type can be determined based on the target image acquired by the unmanned aerial vehicle at first. Then, a target training model for processing the images of the traffic anomaly type is determined from a preset training model set based on the traffic anomaly type, and an image processing result is obtained. In addition, the degree of influence on the vehicles of different travel information due to different types of traffic abnormalities is also different. Therefore, the method and the device can determine the target vehicle needing to plan the driving path again by combining the travel information of the candidate vehicle and the image processing result of the target image by the target training model, and push the navigation information of the target vehicle, wherein the navigation information is used for prompting the target vehicle to plan the driving path again. According to the technical scheme, the influence results of different types of traffic abnormalities on the traffic states of different vehicles can be determined, and the navigation information can be pushed to the target vehicle with the influenced traffic state so as to prompt the target vehicle to replan the driving path. Therefore, when traffic abnormity occurs, the driver of the target vehicle can bypass the abnormal traffic road section according to the prompt of the navigation information, and the occurrence of traffic jam can be avoided.
In summary, as shown in fig. 3, an embodiment of the present application further provides a method for pushing navigation information, including S301 to S304:
s301, determining that a traffic accident occurs based on the target image acquired by at least one unmanned aerial vehicle.
S302, determining a target training model from a preset training model set, and processing a target image by using the target training model to obtain the predicted processing time length and the accident position of the traffic accident.
And S303, determining whether the candidate vehicle passes through the accident position in the estimated processing time length according to the travel information.
And S304, if the passing accident position of the candidate vehicle in the estimated processing time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, as shown in fig. 4, an embodiment of the present application further provides a method for pushing navigation information, including S401-S404:
s401, determining that the road is obstructed based on the target image acquired by the at least one unmanned aerial vehicle.
S402, determining a target training model from a preset training model set, and processing a target image by adopting the target training model to obtain the position and the influence result of the obstacle.
And S403, determining whether the candidate vehicle passes through the position of the obstacle in the target time length according to the travel information under the condition that the influence result is that the vehicle passes through the obstacle.
S404, if the position of the candidate vehicle passing through the obstacle in the preset time length is determined, the candidate vehicle is determined as the target vehicle.
Alternatively, as shown in fig. 5, step S101 in fig. 1 may be replaced with S1011-S1014:
and S1011, determining the target number of the unmanned aerial vehicles scheduled for the current area according to the number of the signed vehicles and/or the current traffic flow in the current area.
And S1012, scheduling the unmanned aerial vehicles for the current area according to the target number and the initial number of the unmanned aerial vehicles in the current area.
And S1013, controlling the unmanned aerial vehicle in the current region after scheduling to acquire the target image based on the preset navigation path in the current region.
And S1014, determining the traffic abnormity type according to the target image.
Optionally, as shown in fig. 6, before step S103 in fig. 1, S1031 to S1032 may be further included:
and S1031, performing feature extraction on the target image, and acquiring the vehicle identity in the target image.
S1032, when the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database, determining the vehicle corresponding to the vehicle identity in the target image as a candidate vehicle.
As shown in fig. 7, an embodiment of the present application further provides a navigation information pushing apparatus, where the navigation information pushing apparatus may include: a determining module 11 and a pushing module 12.
The determining module 11 executes S101 and S102 in the above method embodiment, and the pushing module 12 executes S103 in the above method embodiment.
Specifically, the determining module 11 is configured to determine a traffic anomaly type based on a target image acquired by at least one unmanned aerial vehicle;
the determining module 11 is further configured to determine a target training model from a preset training model set according to the traffic anomaly type, and process a target image by using the target training model to obtain an image processing result;
the pushing module 12 is used for determining a target vehicle according to the travel information of the candidate vehicle and the image processing result determined by the determining module 11, and pushing navigation information to the target vehicle; the navigation information is used for prompting the target vehicle to replan the driving path.
Optionally, in a possible design manner, the image processing result at least includes a traffic anomaly location, and the determining module 11 is specifically configured to:
determining whether the candidate vehicle passes through the abnormal traffic position within the target time length according to the travel information;
and if the traffic abnormal position of the candidate vehicle passing through the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design, when the traffic abnormality type is that an obstacle appears on a road, the traffic abnormality position is a position of the obstacle, the image processing result further includes a result of an influence of the obstacle on vehicle passing, and the determining module 11 is specifically configured to:
determining whether the candidate vehicle passes through the position of the obstacle in the target time length according to the travel information under the condition that the influence result is that the vehicle passes through is influenced;
and if the position of the candidate vehicle passing through the obstacle in the target time length is determined, determining the candidate vehicle as the target vehicle.
Optionally, in another possible design, the determining module 11 is specifically configured to:
determining the target number of unmanned aerial vehicles scheduled for the current area according to the number of signed vehicles and/or the current traffic flow in the current area;
scheduling the unmanned aerial vehicles for the current area according to the target number and the initial number of the unmanned aerial vehicles in the current area;
controlling the unmanned aerial vehicle in the current region to acquire a target image based on a preset navigation path in the current region after scheduling;
and determining the traffic abnormality type according to the target image.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a feature extraction module;
the characteristic extraction module is used for extracting the characteristics of the target image to acquire the vehicle identity identifier in the target image before the determination module 11 determines the target vehicle according to the travel information of the candidate vehicle and the image processing result;
the determining module 11 is further configured to, in a case that the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database, determine the vehicle corresponding to the vehicle identity in the target image as the candidate vehicle.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a deleting module and a sending module;
the deleting module is used for deleting the stored target image according to a preset rule under the condition that no traffic abnormality is determined based on the target image;
or the deleting module is used for calling the sending module to send a first control instruction to the unmanned aerial vehicle for collecting the target image; the first control instruction is used for indicating the unmanned aerial vehicle for acquiring the target image to delete the stored target image according to a preset rule.
Optionally, in another possible design, the navigation information pushing apparatus provided by the present application may further include: a sending module;
the determining module 11 is configured to determine a target unmanned aerial vehicle according to task information of at least one unmanned aerial vehicle when a congested road segment is determined;
the sending module is used for sending a control instruction to the target unmanned aerial vehicle; the control instruction comprises the position of the congested road section, and the control instruction is used for indicating the target unmanned aerial vehicle to fly to the position of the congested road section to acquire the target image.
Optionally, the pushing device of the navigation information may further include a storage module, where the storage module is configured to store the program code of the pushing device of the navigation information, and the like.
As shown in fig. 8, an embodiment of the present application further provides a device for pushing navigation information, which includes a memory 41, processors 42(42-1 and 42-2), a bus 43, and a communication interface 44; the memory 41 is used for storing computer execution instructions, and the processor 42 is connected with the memory 41 through a bus 43; when the pushing device of the navigation information is running, the processor 42 executes the computer-executable instructions stored in the memory 41 to make the pushing device of the navigation information execute the pushing method of the navigation information provided as the above-mentioned embodiment.
In particular implementations, processor 42 may include one or more Central Processing Units (CPUs), such as CPU0 and CPU1 shown in FIG. 8, as one embodiment. And as an example, the pushing means of the navigation information may comprise a plurality of processors 42, such as processor 42-1 and processor 42-2 shown in fig. 8. Each of the processors 42 may be a single-Core Processor (CPU) or a multi-Core Processor (CPU). Processor 42 may refer herein to one or more devices, circuits, and/or processing cores that process data (e.g., computer program instructions).
The memory 41 may be, but is not limited to, a read-only memory 41 (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 41 may be self-contained and coupled to the processor 42 via a bus 43. The memory 41 may also be integrated with the processor 42.
In a specific implementation, the memory 41 is used for storing data in the present application and computer-executable instructions corresponding to software programs for executing the present application. The processor 42 may navigate various functions of the push device by running or executing software programs stored in the memory 41 and invoking data, navigation information, stored in the memory 41.
The communication interface 44 is any device, such as a transceiver, for communicating with other devices or communication networks, such as a control system, a Radio Access Network (RAN), a Wireless Local Area Network (WLAN), and the like. The communication interface 44 may include a receiving unit implementing a receiving function and a transmitting unit implementing a transmitting function.
The bus 43 may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an extended ISA (enhanced industry standard architecture) bus, or the like. The bus 43 may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 8, but this is not intended to represent only one bus or type of bus.
As an example, in connection with fig. 7, the determining module in the pushing device of the navigation information implements the same function as the processor in fig. 8, and the storing module in the pushing device of the navigation information implements the same function as the memory in fig. 8.
For the explanation of the related contents in this embodiment, reference may be made to the above method embodiments, which are not described herein again.
Through the above description of the embodiments, it is clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the device may be divided into different functional modules to complete all or part of the above described functions. For the specific working processes of the system, the apparatus and the unit described above, reference may be made to the corresponding processes in the foregoing method embodiments, and details are not described here again.
The embodiment of the present application further provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed by a computer, the computer is enabled to execute the method for pushing navigation information provided in the foregoing embodiment.
The computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an erasable programmable read-only memory (EPROM), a register, a hard disk, an optical fiber, a CD-ROM, an optical storage device, a magnetic storage device, any suitable combination of the foregoing, or any other form of computer readable storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an Application Specific Integrated Circuit (ASIC). In embodiments of the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The above description is only an embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions within the technical scope of the present disclosure should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for pushing navigation information is characterized by comprising the following steps:
determining a traffic anomaly type based on a target image acquired by at least one unmanned aerial vehicle;
determining a target training model from a preset training model set according to the traffic anomaly type, and processing the target image by adopting the target training model to obtain an image processing result;
determining a target vehicle according to the travel information of the candidate vehicle and the image processing result, and pushing navigation information to the target vehicle; and the navigation information is used for prompting the target vehicle to replan the driving path.
2. The method according to claim 1, wherein the image processing result at least includes a traffic abnormal position, and the determining a target vehicle according to the travel information of the candidate vehicle and the image processing result comprises:
determining whether the candidate vehicle passes through the abnormal traffic position within a target time length according to the travel information;
and if the candidate vehicle is determined to pass through the abnormal traffic position within the target time length, determining the candidate vehicle as the target vehicle.
3. The navigation information pushing method according to claim 2, wherein in a case where the traffic abnormality type is that an obstacle occurs on a road, the traffic abnormality position is a position of the obstacle; the image processing result also comprises an influence result of the obstacle on vehicle passing;
the determining a target vehicle according to the trip information of the candidate vehicle and the image processing result includes:
determining whether the candidate vehicle passes through the position of the obstacle within the target time length according to the travel information under the condition that the influence result is that the vehicle passes through is influenced;
and if the position of the candidate vehicle passing through the obstacle in the target time length is determined, determining the candidate vehicle as the target vehicle.
4. The method for pushing navigation information according to claim 1, wherein the determining of the traffic anomaly type based on the target image acquired by the at least one drone comprises:
determining the target number of unmanned aerial vehicles scheduled for the current area according to the number of signed vehicles and/or the current traffic flow in the current area;
scheduling the unmanned aerial vehicles for the current area according to the target number and the initial number of the unmanned aerial vehicles in the current area;
controlling the unmanned aerial vehicle in the current area to acquire the target image in the current area based on a preset navigation path after scheduling;
and determining the traffic abnormality type according to the target image.
5. The method according to claim 1, wherein before determining the target vehicle based on the travel information of the candidate vehicle and the image processing result, the method further comprises:
extracting the characteristics of the target image to obtain a vehicle identity in the target image;
and under the condition that the vehicle identity in the target image is successfully matched with the vehicle identity in the identification database, determining the vehicle corresponding to the vehicle identity in the target image as a candidate vehicle.
6. The method for pushing navigation information according to claim 1, further comprising:
deleting the stored target image according to a preset rule under the condition that no traffic abnormality is determined based on the target image;
or sending a first control instruction to the unmanned aerial vehicle for acquiring the target image; the first control instruction is used for indicating the unmanned aerial vehicle which collects the target image to delete the stored target image according to a preset rule.
7. The method for pushing the navigation information according to any one of claims 1 to 6, further comprising:
under the condition that the congested road section is determined, determining a target unmanned aerial vehicle according to the task information of the at least one unmanned aerial vehicle;
sending a second control instruction to the target unmanned aerial vehicle; the second control instruction comprises the position of the congested road section, and the second control instruction is used for indicating the target unmanned aerial vehicle to fly to the position of the congested road section to acquire the target image.
8. A navigation information pushing apparatus, comprising:
the determining module is used for determining the traffic abnormity type based on the target image acquired by at least one unmanned aerial vehicle;
the determining module is further used for determining a target training model from a preset training model set according to the traffic anomaly type, and processing the target image by adopting the target training model to obtain an image processing result;
the pushing module is used for determining a target vehicle according to the travel information of the candidate vehicle and the image processing result determined by the determining module and pushing navigation information to the target vehicle; and the navigation information is used for prompting the target vehicle to replan the driving path.
9. The pushing device of navigation information is characterized by comprising a memory, a processor, a bus and a communication interface; the memory is used for storing computer execution instructions, and the processor is connected with the memory through the bus;
when the pushing device of the navigation information is operated, the processor executes the computer-executable instructions stored in the memory to cause the pushing device of the navigation information to execute the pushing method of the navigation information according to any one of claims 1 to 7.
10. A computer-readable storage medium having stored therein instructions, which when executed by a computer, cause the computer to execute a method of pushing navigation information according to any one of claims 1 to 7.
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